Determining the placement of wells is an important step in exploration and production management. Well placement affects the performance and viability of a field over its entire production life. However, determining optimum well placement, or even good well placement, is a complex problem. For example, the geology and geomechanics of subsurface conditions influence both drilling cost and where wells can be reliably placed. Well trajectories must also avoid those of existing wells. Further, wells have practical drilling and construction constraints. Constraints also exist at the surface, including but not limited to bathymetric and topographic constraints, legal constraints, and constraints related to existing facilities such as platforms and pipelines. Finally, financial uncertainty can affect the viability of different solutions over time.
There is a relatively long history of research activity associated with development of automated and semi-automated computation of field development plans (FDPs). Most or all studies recognize that this particular optimization problem is highly combinatorial and non-linear. Early work such as Rosenwald, G. W., Green, D. W., 1974, A Method for Determining the Optimum Location of Wells in a Reservoir Using Mixed-Integer Programming, Society of Petroleum Engineering Journal 14 (1), 44-54; and Beckner, B. L., Song, X., 1995, Field Development Planning Using Simulated Annealing, SPE 30650; and Santellani, G., Hansen, B., Herring, T., 1998, “Survival of the Fittest” an Optimized Well Location Algorithm for Reservoir Simulation, SPE 39754; and Ierapetritou, M. G., Floudas, C. A., Vasantharajan, S., Cullick, A. S., 1999, A Decomposition Based Approach for Optimal Location of Vertical Wells in American Institute of Chemical Engineering Journal 45 (4), pp. 844-859 is based on mixed-integer programming approaches. While this work is pioneering in the area, it principally focuses on vertical wells and relatively simplistic static models. More recently, work has been published on a Hybrid Genetic Algorithm (“HGA”) technique for calculation of FDPs that include non-conventional, i.e., non-vertical, wells and sidetracks. Examples of such work include Guiyaguler, B., Home, R. N., Rogers, L., 2000, Optimization of Well Placement in a Gulf of Mexico Waterflooding Project, SPE 63221; and Yeten, B., Durlofsky, L. J., Aziz, K., 2002, Optimization of Nonconventional Well Type, Location and Trajectory, SPE 77565; and Badra, O., Kabir, C. C., 2003, Well Placement Optimization in Field Development, SPE 84191; and Guiyaguler, B., Home, R. N., 2004, Uncertainty Assessment of Well Placement Optimization, SPE 87663. While the HGA technique is relatively efficient, the underlying well model is still relatively simplistic, e.g., one vertical segment down to a kick-off depth (heal), then an optional deviated segment extending to the toe. The sophistication of optimized FDPs based on the HGA described above has grown in the past few years as the time component is being included to support injectors, and uncertainty in the reservoir model is being considered. Examples include Cullick, A. S., Heath, D., Narayanan, K., April, J., Kelly, J., 2003, Optimizing multiple-field scheduling and production strategy with reduced risk, SPE 84239; and Cullick, A. S., Narayanan, K., Gorell, S., 2005, Optimal Field Development Planning of Well Locations With Reservoir Uncertainty, SPE 96986. However, improved automated calculation of FDPs remains desirable.